@inproceedings{120f94f534724267b2b3514a3db37477,
title = "Radar technical language modeling with named entity recognition and text classification",
abstract = "This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.",
author = "Zaunegger, {Jackson S.} and Singerman, {Paul G.} and Narayanan, {Ram M.} and O'Rourke, {Sean M.} and Muralidhar Rangaswamy",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Radar Sensor Technology XXVI 2022 ; Conference date: 06-06-2022 Through 12-06-2022",
year = "2022",
doi = "10.1117/12.2622410",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ranney, {Kenneth I.} and Raynal, {Ann M.}",
booktitle = "Radar Sensor Technology XXVI",
address = "United States",
}